Radar Analyses of the Physics of Extreme Rainfall Events

Extreme precipitation events pose a threat to life, property, and economic growth throughout the United States and across the world. Although extensive research has focused on improving understanding of hydrometeorology at short space and time scales, there is still much that needs to be understood on the subseasonal to seasonal (S2S) timescale. This research uses a database of observed S2S extreme precipitation events in the United States and high-resolution ground-based radar observations to identify the leading source of precipitation, either convective or stratiform, and how it varies based on event type (location, dynamics, season, etc.). S2S precipitation events are defined as 2-week precipitation accumulations that exceed the 95^th percentile accumulation for a given location and time period. A 3-D radar echo classification algorithm was used to objectively stratify precipitation into convective and stratiform components. Rainfall estimates based on multiple radar precipitation rate relationships were calculated and compared to hourly stage IV gauge-corrected precipitation data to determine the sensitivity of diagnosed rainfall amounts and percentage of rainfall from each precipitation source to the precipitation dataset used. The radar rain rate relationships used in this study were: i) the classic reflectivity-rain rate (Z-R) relationship that we varied for convective/stratiform echoes, ii) a polarimetric relationship that depends on Z, differential reflectivity (ZDR), and specific differential phase (KDP), and iii) a polarimetric relationship that depends on specific attenuation (A). To determine the sensitivity of the fraction of total rainfall by precipitation source to the rainfall estimate used (i.e., stage IV, Z–R, Z_ZDR_KDP–R, or A–R), several recent high-impact events were used, including Hurricanes Harvey (2017) and Florence (2018). For the analysis of the larger S2S extreme event dataset, we expect stratiform precipitation to be the primary driver for extreme precipitation events that are characterized by prolonged large-scale forcing and convection to be the primary driver of extreme events characterized by local forcing.